Many applications of distributed sensor networks require a spatial understanding of where the sensors are located. This geographical location information is necessary so that the system can make decisions to observe a phenomenon at a particular location or observe a phenomenon at a number of locations. For example, if one sensor in a network observes a local phenomenon it may be desirable for other nearby sensors to also observe the same phenomenon. Since it usually would not make sense for all sensors in the network to observe the phenomenon, attempts to control such observation under central control are difficult to achieve.
One structure for achieving this result is for a central controller to keep track of all sensor geographical locations and “instruct” one or more sensors in a desired location to make a measurement, observe a phenomenon, perform an action or a combination thereof. This consumes transmission bandwidth as well as processor time and often is not practical. For example, in prior systems a manual determination is made as to the location of all sensors in a network. Then a “neighbor” list is constructed and distributed to all sensors. In addition to being cumbersome, this approach is prone to errors arising from transmission difficulties as well as from using “stale” data.